Transcript of "Personal Environmental Impact Report"

3.
What is PEIR?
 PEIR - Personal Environmental Impact Report
 “Mobile-to-web” app that allows you to use your GPS-equipped
Smartphone to explore, measure and share your own environmental
footprint and how the environment impacts you.
 Uses location data that is regularly and securely uploaded from your
mobile phone to create personalized report about your
environmental impact and exposure.
 Inspired by the Environmental Impact Reports (EIR’s) required for
construction and public work projects.

4.
PEIR Goals
 Provide participants with near real-time feedback on their environmental
impact and exposure.
 Designed to bring specific environmental aspects of our personal lives to
light.
 Help people understand how they can reduce environmental impact by
changing their daily behavior.

5.
History & Background
 Project collaboration between UCLA (University of California, Los
Angeles) and Nokia researchers. Also sponsored by the National
Science Foundation (NSF)
 Under development for about a little over a year at UCLA.
 PEIR has been running under “pilot production mode” since June
2008 with thirty trial users using the system periodically.

6.
PEIR Capabilities
 Uses the mobile phone’s GPS and cell towers to record and upload your
location approximately every thirty seconds.
 Based on your location trace, the system infers your activity (walking,
staying still, driving) and logs it throughout the day.
 PEIR maps this combination of location, time, and activity to regional air
quality and weather data to calculate your personal carbon footprint and
your exposure to fine particulate matter (PM 2.5) in the air
 Results are presented to participants in an interactive graphical user
interface (GUI).
 Metrics can also be shared and compared with other users using a unique
Facebook application.

7.
PEIR Science
 The impact and exposure measures are computed record-by-
record. Each record consists of the following:
 Participant's system ID
 Vehicle Type
 Current location and speed (derived from a users GPS)
 Activity class.

8.
Activity Classification
 Multi-step activity classification:
 Filter out anomalous GPS points
 Match to verify if on a freeway.
 Calculate speed.
 Classify using decision tree.
 Classify using Hidden Markov Model.
 Classify trips: A traveling from one place to the other
where a user stays for more than 10 min.
 Annotate trace with activity. (e.g. still, walk, drive).

9.
Location Tracing
 Location traces are gathered from mobile phones using GPS and
cell towers.
 GPS records are sampled at approximately every 30 seconds .
 Based on these time-location traces, the PEIR system can assume
your activities (walking, still, driving) and log them throughout the
day.

10.
Trace Correction
 Under-sampled location traces are corrected and interpreted using
techniques such as map matching.
 Uses road network and building data to determine if a user is on a
freeway or not.
 Accuracy increases from 40% to 82% when using the freeway
annotation information obtained from the map matching technique.
 PEIR first implemented a modified naive map-matching scheme
called “Intersection-based map-matching”.
 After several months, a new algorithm referred to as “Intersection w/
nearest road and substitution” was developed.

11.
Intersection-Based Map-Matching Algorithm
 Steps:
1. Find the two nearest roads for each data point.
2. If distances from a GPS data point to the two roads are less than
.04 miles, label the GPS data point as the pre-intersection.
Otherwise, add it to the buffer of data points. Continue until the
next intersection point is identified, referred to as the post-
intersection.
3. Compare the pre- and post-intersections and identify the road
that appears in both intersections as the correct match for the
buffered data points. If no common road is found, consider the
subsequent GPS data points to identify an alternative post-
intersection.

12.
Intersection w/ Nearest Road And Substitution
Algorithm
 Modified the previous algorithm to include two new
mechanisms:
1. Consider the nearest road within .04 miles as a possible
intersection point when looking for a common road segment
between two consecutive intersection points.
 Helps correct for the situation when the captured GPS data points are not
near intersections and therefore miss turning points that occur in between
the captured GPS data points.
2. Replace both pre- and post-intersections when there is no
common road between two consecutive intersections.
 Assigns the post-intersection as the new pre-intersection, and using a
subsequent intersection as the new post-intersection.

16.
Carbon Impact
 A measure of transportation-related carbon (CO2) footprint, a
greenhouse gas implicated in climate change, in grams.
 Calculated only for records classified with the activity driving.
 PEIR uses the Emissions Factors Model (EMFAC), a FORTRAN
program developed by the California Air Resources Board (CARB), to
calculate carbon dioxide emissions by a user in units of grams. The
model computes vehicle emissions based on current weather conditions
(temperature and relative humidity), and the speed and type of vehicle.
 Temperature and humidity are grabbed from the closest weather station to a participants
location that reported data in the last hour.
 Speed is derived from a user’s trace (using GPS)
 Type of vehicle is collected at sign-up

17.
Sensitive Site Impact
 A measure of the user's transportation-related
airborne particulate matter emissions (PM 2.5)
near sites with populations sensitive to it, such as
hospitals and schools.
 Pollutants also estimated using EMFAC model.
 Only calculated when user is within 200m of a
sensitive site.
 The total amount of particulate pollution (in
grams) you emit per trip near sensitive sites is
reported in your profile.

18.
Particulate Exposure
 A measure of the user's transportation-related exposure
to particulate matter emissions from other vehicles.
 Also using the EMFAC model, PEIR calculates the
quantity of particulate pollution generated by traffic on
roads within 200 meters.
 Exposure is divided into three categories:
 Low – Fewer than 15 micrograms per cubic meter
 Medium – Between 15 and 65 micrograms per cubic meter
 High – Over 65 micrograms per cubic meter

19.
Fast Food Exposure
 The proximity to fast-food eating establishments.
 Determines whether a fast food establishment is within a
quarter mile.
 The records of a trip are annotated with a flag indicating
whether a fast food joint was nearby.
 PEIR then gathers the total amount of time near a fast food
restaurant per trip.
 Exposure is divided into three categories:
 Low – 3 or fewer restaurants
 Medium – Between 3 and 5 restaurants
 High – Over 5 restaurants

22.
PEIR Participation
 PEIR is currently in private beta
 To participate in PEIR you’ll need the following:
 Mobile phone with a data plan
 GPS capability (either built in to the phone or via an
external GPS device)
 Web access
 The current release of PEIR requires:
 Nokia N80 with external GPS device, or
 Nokia N95 with built in GPS

23.
Future Development
 Adding the ability for users to upload their location traces (GPS
information) via GPX (GPS exchange format). This will allow
people who do not have the phones mentioned in the previous
slide to participate.
 Add device-level authentication and explore taking advantage of
WiFi and Bluetooth stumbling to enhance location trace.
 Add accelerometer data that most of these client platforms are
capable of capturing to enhance activity classification accuracy.
 Become a public branded application

24.
Current Deployment
Example Over 100 students and teenagers in San Francisco,
California, are currently using this service to monitor their
“greenness”.
 Students keep a log of their activity on their mobile device.
○ Personalized profile allows them to track their carbon emissions based
on their transportation choices.
 Students use the application running in their Facebook profile so
that they can use their mobile devices to regularly update and
compare their scores with friends.
 The students reduced their carbon impact by an average of 20%
through an increased use of public transportation, biking, and
carpooling